Density, viscosity and refractive index prediction of binary and ternary mixtures systems of ionic liquid

Authors

  • Gonzalo Astray Dopazo Department of Physical Chemistry, Faculty of Science, University of Vigo at Ourense, Spain
  • Miriam Gonzalez-Temes Department of Physical Chemistry, Faculty of Science, University of Vigo at Ourense, Spain
  • Dina Larios Lopez Department of Geological Sciences, College of Arts and Sciences, Ohio University, Athens, United States of America
  • Juan Carlos Mejuto Department of Physical Chemistry, Faculty of Science, University of Vigo at Ourense, Spain

DOI:

https://doi.org/10.13171/mjc.3.4.2014.25.07.19

Abstract

Different artificial neural networks architectures were developed to predict the density, viscosity and refractive index of binary and ternary mixtures of ionic liquids using their individual properties. All neural network implemented were evaluated using the root mean square error (RMSE) and the average percentage deviations (APD) for the training and validation phase. The individual models implemented show great values of R2 (greater than 9.995∙10-01) and low errors in terms of RMSE, that corresponds with an APD less than 7.766∙10-02 %, with the exception of the model for the prediction of viscosity where the error raised to 3.338 %. The results show that the different individual artificial neural networks implemented are a useful tool to predict the density, viscosity and refractive index of binary and ternary mixtures of ionic liquids and with reasonably accuracy.  

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Published

2014-07-25

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Section

Physical Chemistry